Towards Controllable Video Synthesis of Routine and Rare OR Events
Dominik Schneider, Lalithkumar Seenivasan, Sampath Rapuri, Vishalroshan Anil, Aiza Maksutova, Yiqing Shen, Jan Emily Mangulabnan, Hao Ding, Jose L. Porras, Masaru Ishii, Mathias Unberath

TL;DR
This paper introduces a novel OR video diffusion framework that enables controlled synthesis of routine and rare safety-critical events, facilitating dataset creation and AI model training for better OR safety monitoring.
Contribution
The work presents a new diffusion-based method with geometric abstraction and conditioning modules for realistic, controllable OR event video synthesis, including rare safety-critical scenarios.
Findings
Outperforms baseline diffusion models in quality metrics
Achieves 70.13% recall in safety-critical event detection
Enables generation of counterfactual OR scenarios
Abstract
Purpose: Curating large-scale datasets of operating room (OR) workflow, encompassing rare, safety-critical, or atypical events, remains operationally and ethically challenging. This data bottleneck complicates the development of ambient intelligence for detecting, understanding, and mitigating rare or safety-critical events in the OR. Methods: This work presents an OR video diffusion framework that enables controlled synthesis of rare and safety-critical events. The framework integrates a geometric abstraction module, a conditioning module, and a fine-tuned diffusion model to first transform OR scenes into abstract geometric representations, then condition the synthesis process, and finally generate realistic OR event videos. Using this framework, we also curate a synthetic dataset to train and validate AI models for detecting near-misses of sterile-field violations. Results: In…
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Taxonomy
TopicsHealthcare Technology and Patient Monitoring · Patient Safety and Medication Errors · Adversarial Robustness in Machine Learning
